Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations1000
Missing cells577
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory337.7 KiB
Average record size in memory345.8 B

Variable types

Numeric3
Categorical7

Alerts

Credit amount is highly overall correlated with DurationHigh correlation
Duration is highly overall correlated with Credit amountHigh correlation
Saving accounts has 183 (18.3%) missing values Missing
Checking account has 394 (39.4%) missing values Missing

Reproduction

Analysis started2024-12-14 21:14:39.541782
Analysis finished2024-12-14 21:14:42.977431
Duration3.44 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct53
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.546
Minimum19
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-12-14T21:14:43.027792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q127
median33
Q342
95-th percentile60
Maximum75
Range56
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.375469
Coefficient of variation (CV)0.32002106
Kurtosis0.59577957
Mean35.546
Median Absolute Deviation (MAD)7
Skewness1.0207393
Sum35546
Variance129.40129
MonotonicityNot monotonic
2024-12-14T21:14:43.106483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 51
 
5.1%
26 50
 
5.0%
23 48
 
4.8%
24 44
 
4.4%
28 43
 
4.3%
25 41
 
4.1%
30 40
 
4.0%
35 40
 
4.0%
36 39
 
3.9%
31 38
 
3.8%
Other values (43) 566
56.6%
ValueCountFrequency (%)
19 2
 
0.2%
20 14
 
1.4%
21 14
 
1.4%
22 27
2.7%
23 48
4.8%
24 44
4.4%
25 41
4.1%
26 50
5.0%
27 51
5.1%
28 43
4.3%
ValueCountFrequency (%)
75 2
 
0.2%
74 4
0.4%
70 1
 
0.1%
68 3
 
0.3%
67 3
 
0.3%
66 5
0.5%
65 5
0.5%
64 5
0.5%
63 8
0.8%
62 2
 
0.2%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.5 KiB
male
690 
female
310 

Length

Max length6
Median length4
Mean length4.62
Min length4

Characters and Unicode

Total characters4620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
male 690
69.0%
female 310
31.0%

Length

2024-12-14T21:14:43.179651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:43.236953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
male 690
69.0%
female 310
31.0%

Most occurring characters

ValueCountFrequency (%)
e 1310
28.4%
m 1000
21.6%
a 1000
21.6%
l 1000
21.6%
f 310
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4620
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1310
28.4%
m 1000
21.6%
a 1000
21.6%
l 1000
21.6%
f 310
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1310
28.4%
m 1000
21.6%
a 1000
21.6%
l 1000
21.6%
f 310
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1310
28.4%
m 1000
21.6%
a 1000
21.6%
l 1000
21.6%
f 310
 
6.7%

Job
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
2
630 
1
200 
3
148 
0
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 630
63.0%
1 200
 
20.0%
3 148
 
14.8%
0 22
 
2.2%

Length

2024-12-14T21:14:43.288977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:43.339466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 630
63.0%
1 200
 
20.0%
3 148
 
14.8%
0 22
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2 630
63.0%
1 200
 
20.0%
3 148
 
14.8%
0 22
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 630
63.0%
1 200
 
20.0%
3 148
 
14.8%
0 22
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 630
63.0%
1 200
 
20.0%
3 148
 
14.8%
0 22
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 630
63.0%
1 200
 
20.0%
3 148
 
14.8%
0 22
 
2.2%

Housing
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.2 KiB
own
713 
rent
179 
free
108 

Length

Max length4
Median length3
Mean length3.287
Min length3

Characters and Unicode

Total characters3287
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowown
2nd rowown
3rd rowown
4th rowfree
5th rowfree

Common Values

ValueCountFrequency (%)
own 713
71.3%
rent 179
 
17.9%
free 108
 
10.8%

Length

2024-12-14T21:14:43.400051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:43.453956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
own 713
71.3%
rent 179
 
17.9%
free 108
 
10.8%

Most occurring characters

ValueCountFrequency (%)
n 892
27.1%
o 713
21.7%
w 713
21.7%
e 395
12.0%
r 287
 
8.7%
t 179
 
5.4%
f 108
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3287
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 892
27.1%
o 713
21.7%
w 713
21.7%
e 395
12.0%
r 287
 
8.7%
t 179
 
5.4%
f 108
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 892
27.1%
o 713
21.7%
w 713
21.7%
e 395
12.0%
r 287
 
8.7%
t 179
 
5.4%
f 108
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 892
27.1%
o 713
21.7%
w 713
21.7%
e 395
12.0%
r 287
 
8.7%
t 179
 
5.4%
f 108
 
3.3%

Saving accounts
Categorical

Missing 

Distinct4
Distinct (%)0.5%
Missing183
Missing (%)18.3%
Memory size54.4 KiB
little
603 
moderate
103 
quite rich
63 
rich
 
48

Length

Max length10
Median length6
Mean length6.4430845
Min length4

Characters and Unicode

Total characters5264
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlittle
2nd rowlittle
3rd rowlittle
4th rowlittle
5th rowquite rich

Common Values

ValueCountFrequency (%)
little 603
60.3%
moderate 103
 
10.3%
quite rich 63
 
6.3%
rich 48
 
4.8%
(Missing) 183
 
18.3%

Length

2024-12-14T21:14:43.515368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:43.573900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
little 603
68.5%
rich 111
 
12.6%
moderate 103
 
11.7%
quite 63
 
7.2%

Most occurring characters

ValueCountFrequency (%)
t 1372
26.1%
l 1206
22.9%
e 872
16.6%
i 777
14.8%
r 214
 
4.1%
h 111
 
2.1%
c 111
 
2.1%
m 103
 
2.0%
o 103
 
2.0%
d 103
 
2.0%
Other values (4) 292
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5201
98.8%
Space Separator 63
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1372
26.4%
l 1206
23.2%
e 872
16.8%
i 777
14.9%
r 214
 
4.1%
h 111
 
2.1%
c 111
 
2.1%
m 103
 
2.0%
o 103
 
2.0%
d 103
 
2.0%
Other values (3) 229
 
4.4%
Space Separator
ValueCountFrequency (%)
63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5201
98.8%
Common 63
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1372
26.4%
l 1206
23.2%
e 872
16.8%
i 777
14.9%
r 214
 
4.1%
h 111
 
2.1%
c 111
 
2.1%
m 103
 
2.0%
o 103
 
2.0%
d 103
 
2.0%
Other values (3) 229
 
4.4%
Common
ValueCountFrequency (%)
63
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1372
26.1%
l 1206
22.9%
e 872
16.6%
i 777
14.8%
r 214
 
4.1%
h 111
 
2.1%
c 111
 
2.1%
m 103
 
2.0%
o 103
 
2.0%
d 103
 
2.0%
Other values (4) 292
 
5.5%

Checking account
Categorical

Missing 

Distinct3
Distinct (%)0.5%
Missing394
Missing (%)39.4%
Memory size54.6 KiB
little
274 
moderate
269 
rich
63 

Length

Max length8
Median length6
Mean length6.679868
Min length4

Characters and Unicode

Total characters4048
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlittle
2nd rowmoderate
3rd rowlittle
4th rowlittle
5th rowmoderate

Common Values

ValueCountFrequency (%)
little 274
27.4%
moderate 269
26.9%
rich 63
 
6.3%
(Missing) 394
39.4%

Length

2024-12-14T21:14:43.641637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:43.704391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
little 274
45.2%
moderate 269
44.4%
rich 63
 
10.4%

Most occurring characters

ValueCountFrequency (%)
t 817
20.2%
e 812
20.1%
l 548
13.5%
i 337
8.3%
r 332
8.2%
o 269
 
6.6%
m 269
 
6.6%
d 269
 
6.6%
a 269
 
6.6%
c 63
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4048
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 817
20.2%
e 812
20.1%
l 548
13.5%
i 337
8.3%
r 332
8.2%
o 269
 
6.6%
m 269
 
6.6%
d 269
 
6.6%
a 269
 
6.6%
c 63
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4048
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 817
20.2%
e 812
20.1%
l 548
13.5%
i 337
8.3%
r 332
8.2%
o 269
 
6.6%
m 269
 
6.6%
d 269
 
6.6%
a 269
 
6.6%
c 63
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 817
20.2%
e 812
20.1%
l 548
13.5%
i 337
8.3%
r 332
8.2%
o 269
 
6.6%
m 269
 
6.6%
d 269
 
6.6%
a 269
 
6.6%
c 63
 
1.6%

Credit amount
Real number (ℝ)

High correlation 

Distinct921
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3271.258
Minimum250
Maximum18424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-12-14T21:14:43.774900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile708.95
Q11365.5
median2319.5
Q33972.25
95-th percentile9162.7
Maximum18424
Range18174
Interquartile range (IQR)2606.75

Descriptive statistics

Standard deviation2822.7369
Coefficient of variation (CV)0.86289032
Kurtosis4.2925903
Mean3271.258
Median Absolute Deviation (MAD)1097.5
Skewness1.9496277
Sum3271258
Variance7967843.5
MonotonicityNot monotonic
2024-12-14T21:14:43.864592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1275 3
 
0.3%
1262 3
 
0.3%
1478 3
 
0.3%
1393 3
 
0.3%
1258 3
 
0.3%
1199 2
 
0.2%
2333 2
 
0.2%
1295 2
 
0.2%
1845 2
 
0.2%
1474 2
 
0.2%
Other values (911) 975
97.5%
ValueCountFrequency (%)
250 1
0.1%
276 1
0.1%
338 1
0.1%
339 1
0.1%
343 1
0.1%
362 1
0.1%
368 1
0.1%
385 1
0.1%
392 1
0.1%
409 1
0.1%
ValueCountFrequency (%)
18424 1
0.1%
15945 1
0.1%
15857 1
0.1%
15672 1
0.1%
15653 1
0.1%
14896 1
0.1%
14782 1
0.1%
14555 1
0.1%
14421 1
0.1%
14318 1
0.1%

Duration
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.903
Minimum4
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-12-14T21:14:43.961324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q112
median18
Q324
95-th percentile48
Maximum72
Range68
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.058814
Coefficient of variation (CV)0.57689396
Kurtosis0.91978136
Mean20.903
Median Absolute Deviation (MAD)6
Skewness1.0941842
Sum20903
Variance145.41501
MonotonicityNot monotonic
2024-12-14T21:14:44.048812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
24 184
18.4%
12 179
17.9%
18 113
11.3%
36 83
8.3%
6 75
7.5%
15 64
 
6.4%
9 49
 
4.9%
48 48
 
4.8%
30 40
 
4.0%
21 30
 
3.0%
Other values (23) 135
13.5%
ValueCountFrequency (%)
4 6
 
0.6%
5 1
 
0.1%
6 75
7.5%
7 5
 
0.5%
8 7
 
0.7%
9 49
 
4.9%
10 28
 
2.8%
11 9
 
0.9%
12 179
17.9%
13 4
 
0.4%
ValueCountFrequency (%)
72 1
 
0.1%
60 13
 
1.3%
54 2
 
0.2%
48 48
4.8%
47 1
 
0.1%
45 5
 
0.5%
42 11
 
1.1%
40 1
 
0.1%
39 5
 
0.5%
36 83
8.3%

Purpose
Categorical

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
car
337 
radio/TV
280 
furniture/equipment
181 
business
97 
education
59 
Other values (3)
46 

Length

Max length19
Median length15
Mean length8.559
Min length3

Characters and Unicode

Total characters8559
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowradio/TV
2nd rowradio/TV
3rd roweducation
4th rowfurniture/equipment
5th rowcar

Common Values

ValueCountFrequency (%)
car 337
33.7%
radio/TV 280
28.0%
furniture/equipment 181
18.1%
business 97
 
9.7%
education 59
 
5.9%
repairs 22
 
2.2%
domestic appliances 12
 
1.2%
vacation/others 12
 
1.2%

Length

2024-12-14T21:14:44.144733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:44.227181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
car 337
33.3%
radio/tv 280
27.7%
furniture/equipment 181
17.9%
business 97
 
9.6%
education 59
 
5.8%
repairs 22
 
2.2%
domestic 12
 
1.2%
appliances 12
 
1.2%
vacation/others 12
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r 1035
12.1%
i 856
 
10.0%
e 757
 
8.8%
a 746
 
8.7%
u 699
 
8.2%
n 542
 
6.3%
/ 473
 
5.5%
t 457
 
5.3%
c 432
 
5.0%
o 375
 
4.4%
Other values (13) 2187
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7514
87.8%
Uppercase Letter 560
 
6.5%
Other Punctuation 473
 
5.5%
Space Separator 12
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1035
13.8%
i 856
11.4%
e 757
10.1%
a 746
9.9%
u 699
9.3%
n 542
7.2%
t 457
 
6.1%
c 432
 
5.7%
o 375
 
5.0%
d 351
 
4.7%
Other values (9) 1264
16.8%
Uppercase Letter
ValueCountFrequency (%)
T 280
50.0%
V 280
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 473
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8074
94.3%
Common 485
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1035
12.8%
i 856
10.6%
e 757
9.4%
a 746
9.2%
u 699
 
8.7%
n 542
 
6.7%
t 457
 
5.7%
c 432
 
5.4%
o 375
 
4.6%
d 351
 
4.3%
Other values (11) 1824
22.6%
Common
ValueCountFrequency (%)
/ 473
97.5%
12
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1035
12.1%
i 856
 
10.0%
e 757
 
8.8%
a 746
 
8.7%
u 699
 
8.2%
n 542
 
6.3%
/ 473
 
5.5%
t 457
 
5.3%
c 432
 
5.0%
o 375
 
4.4%
Other values (13) 2187
25.6%

Risk
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
good
700 
bad
300 

Length

Max length4
Median length4
Mean length3.7
Min length3

Characters and Unicode

Total characters3700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowbad
3rd rowgood
4th rowgood
5th rowbad

Common Values

ValueCountFrequency (%)
good 700
70.0%
bad 300
30.0%

Length

2024-12-14T21:14:44.329571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T21:14:44.399177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
good 700
70.0%
bad 300
30.0%

Most occurring characters

ValueCountFrequency (%)
o 1400
37.8%
d 1000
27.0%
g 700
18.9%
b 300
 
8.1%
a 300
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3700
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1400
37.8%
d 1000
27.0%
g 700
18.9%
b 300
 
8.1%
a 300
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3700
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1400
37.8%
d 1000
27.0%
g 700
18.9%
b 300
 
8.1%
a 300
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1400
37.8%
d 1000
27.0%
g 700
18.9%
b 300
 
8.1%
a 300
 
8.1%

Interactions

2024-12-14T21:14:42.586508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:40.241290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.408071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.638453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.246753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.465575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.695057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.353149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T21:14:42.528797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-14T21:14:44.451462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AgeChecking accountCredit amountDurationHousingJobPurposeRiskSaving accountsSex
Age1.0000.0000.026-0.0360.2710.1550.0640.1130.0590.278
Checking account0.0001.0000.0770.0670.0560.0000.1460.1580.1760.000
Credit amount0.0260.0771.0000.6250.1430.1880.1610.1840.0000.098
Duration-0.0360.0670.6251.0000.1400.1210.0710.2180.0000.032
Housing0.2710.0560.1430.1401.0000.1150.1600.1270.0000.228
Job0.1550.0000.1880.1210.1151.0000.1430.0000.0280.073
Purpose0.0640.1460.1610.0710.1600.1431.0000.0810.0360.119
Risk0.1130.1580.1840.2180.1270.0000.0811.0000.1380.066
Saving accounts0.0590.1760.0000.0000.0000.0280.0360.1381.0000.000
Sex0.2780.0000.0980.0320.2280.0730.1190.0660.0001.000

Missing values

2024-12-14T21:14:42.769614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-14T21:14:42.855336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-14T21:14:42.941147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeSexJobHousingSaving accountsChecking accountCredit amountDurationPurposeRisk
067male2ownNaNlittle11696radio/TVgood
122female2ownlittlemoderate595148radio/TVbad
249male1ownlittleNaN209612educationgood
345male2freelittlelittle788242furniture/equipmentgood
453male2freelittlelittle487024carbad
535male1freeNaNNaN905536educationgood
653male2ownquite richNaN283524furniture/equipmentgood
735male3rentlittlemoderate694836cargood
861male1ownrichNaN305912radio/TVgood
928male3ownlittlemoderate523430carbad
AgeSexJobHousingSaving accountsChecking accountCredit amountDurationPurposeRisk
99037male1ownNaNNaN356512educationgood
99134male1ownmoderateNaN156915radio/TVgood
99223male1rentNaNlittle193618radio/TVgood
99330male3ownlittlelittle395936furniture/equipmentgood
99450male2ownNaNNaN239012cargood
99531female1ownlittleNaN173612furniture/equipmentgood
99640male3ownlittlelittle385730cargood
99738male2ownlittleNaN80412radio/TVgood
99823male2freelittlelittle184545radio/TVbad
99927male2ownmoderatemoderate457645cargood